2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSPE-24.3
Paper Title MAEC: Multi-instance learning with an Adversarial Auto-encoder-based Classifier for Speech Emotion Recognition
Authors Changzeng Fu, Osaka University, Japan; Chaoran Liu, Carlos Toshinori Ishi, Advanced Telecommunications Research Institute International, Japan; Hiroshi Ishiguro, Osaka University, Japan
SessionSPE-24: Speech Emotion 2: Neural Networks for Speech Emotion Recognition
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Speech Processing: [SPE-ANLS] Speech Analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, we propose an adversarial auto-encoder-based classifier, which can regularize the distribution of latent representation to smooth the boundaries among categories. Moreover, we adopt multi-instance learning by dividing speech into a bag of segments to capture the most salient moments for presenting an emotion. The proposed model was trained on the IEMOCAP dataset and evaluated on the in-corpus validation set (IEMOCAP) and the cross-corpus validation set (MELD). The experiment results show that our model outperforms the baseline on in-corpus validation and increases the scores on cross-corpus validation with regularization.